Application of Speech Technology in Vehicles

Chen, Fang; Jonsson, Ing-Marie; Villing, Jessica; Larsson, Staffan

doi:10.1007/978-0-387-73819-2_11

Cited by 8 publications

(8 citation statements)

References 28 publications

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“…Bluetooth-based hands-free telephony systems were introduced into the vehicle's infotainment systems in the 1990s enabling drivers to make phone calls wirelessly using the vehicle's embedded microphone and loudspeaker while driving [12]. The common practice for echo cancelation in vehicles is to use an NLMS-based adaptive AEC combined with a nonlinear AES as a post-processor to further attenuate the residual echoes [2,12,13].…”

Section: Aec In the Automotive Contextmentioning

confidence: 99%

An acoustic echo canceller optimized for hands-free speech telecommunication in large vehicle cabins

Saremi,

Ramkumar,

Ghaffari

et al. 2023

J AUDIO SPEECH MUSIC PROC.

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Acoustic echo cancelation (AEC) is a system identification problem that has been addressed by various techniques and most commonly by normalized least mean square (NLMS) adaptive algorithms. However, performing a successful AEC in large commercial vehicles has proved complicated due to the size and challenging variations in the acoustic characteristics of their cabins. Here, we present a wideband fully linear time domain NLMS algorithm for AEC that is enhanced by a statistical double-talk detector (DTD) and a voice activity detector (VAD). The proposed solution was tested in four main Volvo truck models, with various cabin geometries, using standard Swedish hearing-in-noise (HINT) sentences in the presence and absence of engine noise. The results show that the proposed solution achieves a high echo return loss enhancement (ERLE) of at least 25 dB with a fast convergence time, fulfilling ITU G.168 requirements. The presented solution was particularly developed to provide a practical compromise between accuracy and computational cost to allow its real-time implementation on commercial digital signal processors (DSPs). A real-time implementation of the solution was coded in C on an ARM Cortex M-7 DSP. The algorithmic latency was measured at less than 26 ms for processing each 50-ms buffer indicating the computational feasibility of the proposed solution for real-time implementation on common DSPs and embedded systems with limited computational and memory resources. MATLAB source codes and related audio files are made available online for reference and further development.

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Section: Aec In the Automotive Contextmentioning

confidence: 99%

An acoustic echo canceller optimized for hands-free speech telecommunication in large vehicle cabins

Saremi,

Ramkumar,

Ghaffari

et al. 2023

J AUDIO SPEECH MUSIC PROC.

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“…Yet another application scenario, that of future in-car recommender systems, is sketched in [81]. Given the specific situation in a driving scenario, the use of speech technology often is advisable [22], which almost naturally leads to conversational recommendation approaches, e.g., for driving-related aspects like navigation or entertainment [8,9].…”

Section: Application Environmentmentioning

confidence: 99%

A Survey on Conversational Recommender Systems

Jannach,

Manzoor,

Cai

et al. 2020

Preprint

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Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future. CCS Concepts: • Information systems → Recommender systems; • General and reference → Surveys and overviews; • Human-centered computing → Interactive systems and tools;

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“…There are two main approaches to combine several modalities: the parallel approach and the integrated approach [28]. There are two main approaches to combine several modalities: the parallel approach and the integrated approach [28].…”

Section: Modality Issuesmentioning

confidence: 99%